100+ datasets found
  1. Great Smoky Mountains National Park Herbaceous Phenology Database

    • catalog.data.gov
    Updated Nov 25, 2025
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    National Park Service (2025). Great Smoky Mountains National Park Herbaceous Phenology Database [Dataset]. https://catalog.data.gov/dataset/great-smoky-mountains-national-park-herbaceous-phenology-database
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    Dataset updated
    Nov 25, 2025
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Great Smoky Mountains
    Description

    Wildflower phenology data recorded from 13 plots of 2 meter square, at The Purchase area and near Chimneys Picnic Area. Most data involve species in bloom and number of blooms per species per square, but other phenophases are also recorded on flowering and non-flowering plants for most plots. Chimneys Picnic Area data extends back to 2000, Purchase data to 2011 (previously certified).

  2. Comparison of Database Documentation Tools

    • blog.devart.com
    html
    Updated May 13, 2024
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    Devart (2024). Comparison of Database Documentation Tools [Dataset]. https://blog.devart.com/best-database-documentation-tools.html
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    htmlAvailable download formats
    Dataset updated
    May 13, 2024
    Dataset authored and provided by
    Devart
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Tool/Criteria, Supported DBMS, Pricing starts from, Documentation format, Ease of use (max. 4), Customization options (max. 4)
    Description

    A comparison table of popular database documentation tools, including supported DBMS, documentation formats, ease of use, customization options, and pricing.

  3. Water Chemistry (Great Lakes Nearshore Areas)

    • data.ontario.ca
    • catalogue.arctic-sdi.org
    • +1more
    pdf, xlsx, zip
    Updated Jul 10, 2025
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    Environment, Conservation and Parks (2025). Water Chemistry (Great Lakes Nearshore Areas) [Dataset]. https://data.ontario.ca/dataset/water-chemistry-great-lakes-nearshore-areas
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    zip(None), pdf(None), xlsx(None)Available download formats
    Dataset updated
    Jul 10, 2025
    Dataset provided by
    Ministry of the Environment, Conservation and Parkshttp://www.ontario.ca/ministry-environment-and-climate-change
    Authors
    Environment, Conservation and Parks
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Area covered
    Ontario
    Description

    Data is collected each year, according to the lake-by-lake cycle.

    Information includes:

    • water chemistry
    • physical conditions including light profiles
    • approximately 80 index and reference stations throughout the Great Lakes basin
  4. U

    Species of Greatest Conservation Need National Database

    • data.usgs.gov
    • catalog.data.gov
    Updated Oct 22, 2024
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    Tristan Wellman; Elizabeth Martin; Abigail Benson (2024). Species of Greatest Conservation Need National Database [Dataset]. http://doi.org/10.5066/P9OLCQR1
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    Dataset updated
    Oct 22, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Tristan Wellman; Elizabeth Martin; Abigail Benson
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    2005 - 2022
    Description

    The Species of Greatest Conservation Need National Database is an aggregation of lists from State Wildlife Action Plans. Species of Greatest Conservation Need (SGCN) are wildlife species that need conservation attention as listed in action plans. In this database, we have validated scientific names from original documents against taxonomic authorities to increase consistency among names enabling aggregation and summary. This database does not replace the information contained in the original State Wildlife Action Plans. The database includes SGCN lists from 56 states, territories, and districts, encompassing action plans spanning from 2005 to 2022. State Wildlife Action Plans undergo updates at least once every 10 years by respective wildlife agencies. The SGCN list data from these action plans have been compiled in partnership with individual wildlife management agencies, the United States Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies. The SGCN ...

  5. d

    Data from: Demographic modeling data (including code) at various sites in...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Oct 22, 2025
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    U.S. Geological Survey (2025). Demographic modeling data (including code) at various sites in the Great Basin, USA [Dataset]. https://catalog.data.gov/dataset/demographic-modeling-data-including-code-at-various-sites-in-the-great-basin-usa
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    Dataset updated
    Oct 22, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Great Basin, United States
    Description

    These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

  6. g

    Database for the geologic map of the Great Smoky Mountains National Park...

    • gimi9.com
    Updated Oct 18, 2022
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    (2022). Database for the geologic map of the Great Smoky Mountains National Park region, Tennessee and North Carolina | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_database-for-the-geologic-map-of-the-great-smoky-mountains-national-park-region-tennessee-/
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    Dataset updated
    Oct 18, 2022
    Area covered
    Tennessee, Great Smoky Mountains
    Description

    The geologic map database of the Great Smoky Mountains National Park region of Tennessee and North Carolina is a result from studies from 1993 to 2003 as part of a cooperative investigation by the U.S. Geological Survey with the National Park Service (NPS). This work resulted in a 1:100,000-scale geologic map derived from mapping that was conducted at scales of 1:24,000 and 1:62,500. The geologic data are intended to support cooperative investigations with the NPS, the development of a new soil map by the Natural Resources Conservation Service, and the All Taxa Biodiversity Inventory. In response to a request by the NPS, we mapped previously unstudied areas, revised the geology where problems existed, and developed a map database for use in interdisciplinary research, land management, and interpretive programs for park visitors.

  7. d

    Great Smoky Mountains National Park Rhododendron Monitoring Database

    • datasets.ai
    • catalog.data.gov
    • +1more
    57
    Updated May 31, 2023
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    Department of the Interior (2023). Great Smoky Mountains National Park Rhododendron Monitoring Database [Dataset]. https://datasets.ai/datasets/great-smoky-mountains-national-park-rhododendron-monitoring-database
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    57Available download formats
    Dataset updated
    May 31, 2023
    Dataset authored and provided by
    Department of the Interior
    Area covered
    Great Smoky Mountains
    Description

    Data is an MS Access database of tables of in the field health evaluation of rhododendron (Rhododendron maximum) at one location that has a history of rhododendron decline.

  8. g

    NOAA/WDS Paleoclimatology - The Great Barrier Reef Coral Skeletal Records...

    • gimi9.com
    Updated Jul 1, 2024
    + more versions
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    (2024). NOAA/WDS Paleoclimatology - The Great Barrier Reef Coral Skeletal Records Database (GBRCD) | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_noaa-wds-paleoclimatology-the-great-barrier-reef-coral-skeletal-records-database-gbrcd/
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    Dataset updated
    Jul 1, 2024
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Great Barrier Reef
    Description

    This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Coral. The data include parameters of corals and sclerosponges with a geographic location of Australia. The time period coverage is from 7835 to -67 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

  9. d

    Great Lakes Research Vessel Operations 1958-2018: Reference. (ver. 3.0,...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Oct 30, 2025
    + more versions
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    U.S. Geological Survey (2025). Great Lakes Research Vessel Operations 1958-2018: Reference. (ver. 3.0, April 2019) [Dataset]. https://catalog.data.gov/dataset/great-lakes-research-vessel-operations-1958-2018-reference-ver-3-0-april-2019
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    Dataset updated
    Oct 30, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    The Great Lakes
    Description

    The RVCAT database contains data that have been collected on various vessel operations on the Great Lakes and select connecting waterways. This section of Reference Tables specifically handles repetitive or standardized information that is called upon in the main tables of the RVCAT database. Reference tables are used in database design in order to standardize often used values and to make the data file efficient. All of the terms defined in the reference tables have been determined by the United States Geological Survey, Great Lakes Science Center and it’s partners. Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time of release. Also, a large section of this database includes legacy data that if issues arise for cannot be addressed, but nevertheless adds great value to the database. When approaching the following data release, it is strongly suggested to approach the Great Lakes Science Center's researchers for input. Distribution Liability Statement: Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.

  10. Identifiers for the 21st century: How to design, provision, and reuse...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Jun 1, 2023
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    Julie A. McMurry; Nick Juty; Niklas Blomberg; Tony Burdett; Tom Conlin; Nathalie Conte; Mélanie Courtot; John Deck; Michel Dumontier; Donal K. Fellows; Alejandra Gonzalez-Beltran; Philipp Gormanns; Jeffrey Grethe; Janna Hastings; Jean-Karim Hériché; Henning Hermjakob; Jon C. Ison; Rafael C. Jimenez; Simon Jupp; John Kunze; Camille Laibe; Nicolas Le Novère; James Malone; Maria Jesus Martin; Johanna R. McEntyre; Chris Morris; Juha Muilu; Wolfgang Müller; Philippe Rocca-Serra; Susanna-Assunta Sansone; Murat Sariyar; Jacky L. Snoep; Stian Soiland-Reyes; Natalie J. Stanford; Neil Swainston; Nicole Washington; Alan R. Williams; Sarala M. Wimalaratne; Lilly M. Winfree; Katherine Wolstencroft; Carole Goble; Christopher J. Mungall; Melissa A. Haendel; Helen Parkinson (2023). Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data [Dataset]. http://doi.org/10.1371/journal.pbio.2001414
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Julie A. McMurry; Nick Juty; Niklas Blomberg; Tony Burdett; Tom Conlin; Nathalie Conte; Mélanie Courtot; John Deck; Michel Dumontier; Donal K. Fellows; Alejandra Gonzalez-Beltran; Philipp Gormanns; Jeffrey Grethe; Janna Hastings; Jean-Karim Hériché; Henning Hermjakob; Jon C. Ison; Rafael C. Jimenez; Simon Jupp; John Kunze; Camille Laibe; Nicolas Le Novère; James Malone; Maria Jesus Martin; Johanna R. McEntyre; Chris Morris; Juha Muilu; Wolfgang Müller; Philippe Rocca-Serra; Susanna-Assunta Sansone; Murat Sariyar; Jacky L. Snoep; Stian Soiland-Reyes; Natalie J. Stanford; Neil Swainston; Nicole Washington; Alan R. Williams; Sarala M. Wimalaratne; Lilly M. Winfree; Katherine Wolstencroft; Carole Goble; Christopher J. Mungall; Melissa A. Haendel; Helen Parkinson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

  11. T

    Central Asia Great Lakes database - temperature (2021)

    • data.tpdc.ac.cn
    • tpdc.ac.cn
    zip
    Updated Sep 1, 2022
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    Tie LIU (2022). Central Asia Great Lakes database - temperature (2021) [Dataset]. http://doi.org/10.11888/Atmos.tpdc.272607
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    zipAvailable download formats
    Dataset updated
    Sep 1, 2022
    Dataset provided by
    TPDC
    Authors
    Tie LIU
    Area covered
    Description

    Data content: temperature data of Nukus irrigation area from January 2021 to December 2021, unit: 0.1 ℃. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Data application achievements and prospects: in the context of climate change, it can be used to analyze the correlation between meteorological elements and groundwater characteristics, and can also be combined with other hydrometeorological data to analyze the temporal and spatial distribution and change characteristics of groundwater. At the same time, it can also be used as basic data for research in related fields such as extreme climate, food production reduction and human health.

  12. G

    Nevada Great Basin Play Fairway Analysis Regional Data

    • gdr.openei.org
    • data.openei.org
    • +5more
    archive, website
    Updated Oct 28, 2015
    + more versions
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    James Faulds; James Faulds (2015). Nevada Great Basin Play Fairway Analysis Regional Data [Dataset]. http://doi.org/10.15121/1225261
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    archive, websiteAvailable download formats
    Dataset updated
    Oct 28, 2015
    Dataset provided by
    Nevada Bureau of Mines and Geology
    USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
    Geothermal Data Repository
    Authors
    James Faulds; James Faulds
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Great Basin, Nevada
    Description

    This project focused on defining geothermal play fairways and development of a detailed geothermal potential map of a large transect across the Great Basin region (96,000 km2), with the primary objective of facilitating discovery of commercial-grade, blind geothermal fields (i.e. systems with no surface hot springs or fumaroles) and thereby accelerating geothermal development in this promising region. Data included in this submission consists of: structural settings (target areas, recency of faulting, slip and dilation potential, slip rates, quality), regional-scale strain rates, earthquake density and magnitude, gravity data, temperature at 3 km depth, permeability models, favorability models, degree of exploration and exploration opportunities, data from springs and wells, transmission lines and wilderness areas, and published maps and theses for the Nevada Play Fairway area.

  13. G

    Great Lakes Basin Integrated Nutrient Dataset (2000-2019)

    • open.canada.ca
    csv, html, txt
    Updated Mar 17, 2022
    + more versions
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    Environment and Climate Change Canada (2022). Great Lakes Basin Integrated Nutrient Dataset (2000-2019) [Dataset]. https://open.canada.ca/data/en/dataset/8eecfdf5-4fbc-43ec-a504-7e4ee41572eb
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    txt, csv, htmlAvailable download formats
    Dataset updated
    Mar 17, 2022
    Dataset provided by
    Environment and Climate Change Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Time period covered
    Oct 1, 1999 - Oct 1, 2019
    Area covered
    The Great Lakes
    Description

    The Great Lakes Basin Integrated Nutrient Dataset compiles and standardizes phosphorus, nitrogen, and suspended solids data collected between the 2000-2019 water years from multiple Canadian and American sources around the Great Lakes. Ultimately, the goal is to enable regional nutrient data analysis within the Great Lakes Basin. This data is not directly used in the Water Quality Monitoring and Surveillance Division tributary load calculations. Data processing steps include standardizing data column and nutrient names, date-time conversion to Universal Time Coordinates, normalizing concentration units to milligram per liter, and reporting all phosphorus and nitrogen compounds 'as phosphorus' or 'as nitrogen'. Data sources include the Environment and Climate Change Canada National Long-term Water Quality Monitoring Data (WQMS), the Provincial (Stream) Water Quality Monitoring Network (PWQMN) of the Ontario Ministry of the Environment, the Grand River Conservation Authority (GRCA) water quality data, and Heidelberg University’s National Center for Water Quality Research (NCWQR) Tributary Loading Program.

  14. Time-Series Data on the Ocean and Great Lakes Economy for Counties, States,...

    • fisheries.noaa.gov
    • catalog.data.gov
    Updated Jan 1, 2022
    + more versions
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    Office for Coastal Management (2022). Time-Series Data on the Ocean and Great Lakes Economy for Counties, States, and the Nation between 2005 and 2019 (Sector and Industry Level) [Dataset]. https://www.fisheries.noaa.gov/inport/item/48034
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    Dataset updated
    Jan 1, 2022
    Dataset provided by
    Office for Coastal Management
    Time period covered
    2005 - 2019
    Area covered
    Description

    Economics: National Ocean Watch (ENOW) contains annual time-series data for about 400 coastal counties, 30 coastal states, and the nation, derived from the Bureau of Labor Statistics and the Bureau of Economic Analysis. It describes 23 industries in six economic sectors that depend on the oceans and Great Lakes and measures four economic indicators: Establishments, Employment, Wages, and Gross...

  15. d

    HCVDB - Hepatitis C Virus Database

    • dknet.org
    • neuinfo.org
    • +2more
    Updated Jan 29, 2022
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    (2022). HCVDB - Hepatitis C Virus Database [Dataset]. http://identifiers.org/RRID:SCR_007703
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    Dataset updated
    Jan 29, 2022
    Description

    THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The euHCVdb is a Hepatitis C Virus database oriented towards protein sequence, structure and function analyses and structural biology of HCV. In order to make the existing HCV databases as complementary as possible, the current developments are coordinated with the other databases (Japan and Los Alamos) as part of an international collaborative effort. It is monthly updated from the EMBL Nucleotide sequence database and maintained in a relational database management system. Programs for parsing the EMBL database flat files, annotating HCV entries, filling up and querying the database used SQL and Java programming languages. Great efforts have been made to develop a fully automatic annotation procedure thanks to a reference set of HCV complete annotated well-characterized genomes of various genotypes. This automatic procedure ensures standardization of nomenclature for all entries and provides genomic regions/proteins present in the entry, bibliographic reference, genotype, interesting sites or domains, source of the sequence and structural data that are available as protein 3D models. Hepatitis C, Hepatitis C Virus, Hepatitis C Virus protein .

  16. A

    Regional Slip Tendency Analysis of the Great Basin Region

    • data.amerigeoss.org
    • gdr.openei.org
    • +3more
    application/unknown
    Updated Sep 30, 2013
    + more versions
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    United States (2013). Regional Slip Tendency Analysis of the Great Basin Region [Dataset]. https://data.amerigeoss.org/fi/dataset/regional-slip-tendency-analysis-of-the-great-basin-region
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    application/unknownAvailable download formats
    Dataset updated
    Sep 30, 2013
    Dataset provided by
    United States
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Great Basin
    Description

    Slip and dilation tendency on the Great Basin fault surfaces (from the USGS Quaternary Fault Database) were calculated using 3DStress (software produced by Southwest Research Institute).

    Slip and dilation tendency are both unitless ratios of the resolved stresses applied to the fault plane by the measured ambient stress field. - Values range from a maximum of 1 (a fault plane ideally oriented to slip or dilate under ambient stress conditions) to zero (a fault plane with no potential to slip or dilate). - Slip and dilation tendency values were calculated for each fault in the Great Basin. As dip is unknown for many faults in the USGS Quaternary Fault Database, we made these calculations using the dip for each fault that would yield the maximum slip or dilation tendency. As such, these results should be viewed as maximum slip and dilation tendency. - The resulting along-fault and fault-to-fault variation in slip or dilation potential is a proxy for along-fault and fault-to-fault variation in fluid flow conduit potential.

    Stress Magnitudes and directions were calculated across the entire Great Basin. Stress field variation within each focus area was approximated based on regional published data and the world stress database (Hickman et al., 2000; Hickman et al., 1998 Robertson-Tait et al., 2004; Hickman and Davatzes, 2010; Davatzes and Hickman, 2006; Blake and Davatzes 2011; Blake and Davatzes, 2012; Moeck et al., 2010; Moos and Ronne, 2010 and Reinecker et al., 2005).

    The minimum horizontal stress direction (Shmin) was contoured, and spatial bins with common Shmin directions were calculated. Based on this technique, we subdivided the Great Basin into nine regions (Shmin <070, 070

    For faults within the Great Basin proper, we applied a normal faulting stress regime, where the vertical stress (sv) is larger than the maximum horizontal stress (shmax), which is larger than the minimum horizontal stress (sv>shmax>shmin). Based on visual inspection of the limited stress magnitude data in the Great Basin, we used magnitudes such that shmin/shmax = .527 and shmin/sv= .46. These values are consistent with stress magnitude data at both Dixie Valley (Hickman et al., 2000) and Yucca Mountain (Stock et al., 1985).

    For faults within the Walker Lane/Eastern California Shear Zone, we applied a strike-slip faulting stress, where shmax > sv > shmin. Upon visual inspection of limited stress magnitude data from the Walker Lane and Eastern California Shear zone, we chose values such that SHmin/SHmax = .46 and Shmin/Sv= .527 representative of the region.

    Results: The results of our slip and dilation tendency analysis are shown in Figures 4 (dilation tendency), 5 (slip tendency) and 6 (slip tendency + dilation tendency). Shmin varies from northwest to east-west trending throughout much of the Great Basin. As such, north- to northeast-striking faults have the highest tendency to slip and to dilate, depending on the local trend of shmin. These results provide a first order filter on faults and fault systems in the Great Basin, affording focusing of local-scale exploration efforts for blind or hidden geothermal resources.

  17. a

    MNIST Database

    • academictorrents.com
    bittorrent
    Updated Oct 14, 2014
    + more versions
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    Christopher J.C. Burges and Yann LeCun and Corinna Cortes (2014). MNIST Database [Dataset]. https://academictorrents.com/details/ce990b28668abf16480b8b906640a6cd7e3b8b21
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    bittorrent(11594722)Available download formats
    Dataset updated
    Oct 14, 2014
    Dataset authored and provided by
    Christopher J.C. Burges and Yann LeCun and Corinna Cortes
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. With some classification methods (particuarly template-based methods, such as SVM and K-nearest neighbors),

  18. B

    Data Rescue & Curation Best Practices Guide

    • borealisdata.ca
    • search.dataone.org
    Updated Apr 19, 2023
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    OCUL Data Community (ODC) Data Rescue Group (2023). Data Rescue & Curation Best Practices Guide [Dataset]. http://doi.org/10.5683/SP2/Y8MQXV
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 19, 2023
    Dataset provided by
    Borealis
    Authors
    OCUL Data Community (ODC) Data Rescue Group
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    The aim of the Data Rescue & Curation Best Practices Guide is to provide an accessible and hands-on approach to handling data rescue and digital curation of at-risk data for use in secondary research. We provide a set of examples and workflows for addressing common challenges with social science survey data that can be applied to other social and behavioural research data. The goal of this guide and set of workflows presented is to improve librarians’ and data curators’ skills in providing access to high-quality, well-documented, and reusable research data. The aspects of data curation that are addressed throughout this guide are adopted from long-standing data library and archiving practices, including: documenting data using standard metadata, file and data organization; using open and software-agnostic formats; and curating research data for reuse.

  19. Z

    Data from: Data Set from GREAT Case Study 1

    • data.niaid.nih.gov
    • data-staging.niaid.nih.gov
    • +1more
    Updated Sep 18, 2024
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    Schuur, Joost; Ower, Jude; Garg, Anchal; Hewage, Pradeep; Hollins, Paul; Griffiths, Dai (2024). Data Set from GREAT Case Study 1 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_12686860
    Explore at:
    Dataset updated
    Sep 18, 2024
    Dataset provided by
    Universidad Internacional De La Rioja
    University of Bolton
    PlanetPlay
    Authors
    Schuur, Joost; Ower, Jude; Garg, Anchal; Hewage, Pradeep; Hollins, Paul; Griffiths, Dai
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This open data set contains the raw CSV files that were generated in the first GREAT case study, carried out in collaboration with UNDP and using the infrastructure developed by PlanetPlay. A merged file is also provided that may be more convenient for some users who wish to carry out their own analysis.

  20. g

    Data from: Grass Carp (Ctenopharyngodon idella) egg capture data from Great...

    • gimi9.com
    • data.usgs.gov
    • +1more
    + more versions
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    Grass Carp (Ctenopharyngodon idella) egg capture data from Great Lakes tributaries, 2021-2022 (ver. 1.1, November 2023) [Dataset]. https://gimi9.com/dataset/data-gov_grass-carp-ctenopharyngodon-idella-egg-capture-data-from-great-lakes-tributaries-2021-2022/
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    Area covered
    The Great Lakes
    Description

    The data includes dates, places, and times of sampling events for eggs of invasive Grass Carp (Ctenopharyngodon idella) in tributaries to the Great Lakes in 2021 and 2022. Reference data on locations and dates sampled, gears used, and effort are included. Developmental stages for a subset of undamaged, fertilized eggs are provided. Tables include common fields to allow for integration into a relational database to aid data extraction and associating data among tables. First posted: September 2023 Revised: November 2023 (version 1.1)

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National Park Service (2025). Great Smoky Mountains National Park Herbaceous Phenology Database [Dataset]. https://catalog.data.gov/dataset/great-smoky-mountains-national-park-herbaceous-phenology-database
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Great Smoky Mountains National Park Herbaceous Phenology Database

Explore at:
Dataset updated
Nov 25, 2025
Dataset provided by
National Park Servicehttp://www.nps.gov/
Area covered
Great Smoky Mountains
Description

Wildflower phenology data recorded from 13 plots of 2 meter square, at The Purchase area and near Chimneys Picnic Area. Most data involve species in bloom and number of blooms per species per square, but other phenophases are also recorded on flowering and non-flowering plants for most plots. Chimneys Picnic Area data extends back to 2000, Purchase data to 2011 (previously certified).

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